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The Spatial Distribution and Temporal Stability of Soil Water Content During Monsoon Season in Forested Slopes on the

Loess Plateau, China

黃土高原林地坡面雨季期土壤水分空間 分佈及時間穩性

TIAN WANG

王　　添State Key Laboratory of

Eco-hydraulics in Northwest Arid Region of China, Xi’an

University of Technology, Assistant professor

GUOCE XU*

徐　國　策State Key Laboratory of

Eco-hydraulics in Northwest Arid Region of China, Xi’an

University of Technology, Associate Professor

PENG LI

李　　鵬Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid

Regions, Xi’an University of Technology,

Professor

ZHANBIN LI

李　占　斌State Key Laboratory of

Eco-hydraulics in Northwest Arid Region of China, Xi’an

University of Technology, Professor

ZONGPING REN

任　宗　萍Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid

Regions, Xi’an University of Technology,

Associate Professor

KEXIN LU

魯　克　新State Key Laboratory of

Eco-hydraulics in Northwest Arid Region of China, Xi’an

University of Technology, Associate Professor

YUANYI SU

蘇　遠　逸State Key Laboratory of

Eco-hydraulics in Northwest Arid Region of China, Xi’an

University of Technology, Ph.D. Student

FEICHAO WANG

王　飛　超Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid

Regions, Xi’an University of Technology,

Ph.D. Student

ABSTRACT

Abstract: Altitudinal patterns in soil water content (SWC) have an important role in ecological and hydrological processes. SWC is the primary restrictive factor affects the vegetation restoration on the Loess Plateau, China. It is necessary to fully clarify the changes in temporal and spatial stability of SWC within the whole slope which facilitate the understandings of SWC dynamics that affect the vegetation restoration. The current research investigated the spatial distribution of SWC, its temporal stability, and to evaluate the factors that affect SWC temporal stability on a forested slope of the Loess Plateau during the monsoon season. The SWC at interval of 0.2 m within a 0–1.6 m soil vertical profile was measured at 21 positions on the hillslope with the growth Chinese pine (Pinus tabulaeformis

* Corresponding author: Associate Professor / State Key Laboratory of Eco-hydraulics in Northwest Arid Region of ChinaXi’an University of Technology, China / Xi’an University of Technology, Xi’an, Shannxi 710048, China / xuguoce_x@163.com

臺灣水利　第 68 卷　第 2 期民國 109 年 6 月出版

Taiwan Water ConservancyVol. 68, No. 2, June 2020DOI: 10.6937/TWC.202006/PP_68(2).0008

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1. INTRODUCTION

Altitudinal patterns of soil water content (SWC) have an critical effect to the hydrological and vegetation restoration processes, which controls soil erosion, runoff, infiltration, evapotranspiration, nutrient transport and vegetation productivity, especially in arid and semi-arid regions (Heathman et al., 2009; Chaney et al., 2015). The availability of water to plants is also directly controlled by the SWC (Grayson et al., 1998; Jia et al., 2013). In semiarid areas, vegetation restoration and crop productivity are seriously limited by the

SWC (de Souza et al., 2011; Gao et al., 2015). Spatial variability as the factor affecting soil water availability (i.e., soil properties, topography, vegetation, solar radiation, meteorological forcing, and climatic conditions) also results in the spatial-temporal heterogeneity of SWC in different scales (Western et al., 1999; Gómez-Plaza et al., 2001).

Although SWC shows high variability on different time and spatial scales, its level does not necessarily alter with time at the sample locations in spatial pattern (Vachaud et al., 1985; Schneider et al., 2008; Hu et al., 2010; Joshi et al., 2011; Liu et al., 2014). Vachaud et al. (1985) was the first to

Carr). The result showed that average SWCs at all soil depths were low and demonstrated moderate spatial variability. SWC in three soil depths were observed: SWC increases when the depth was 0 to 0.6 m; decreases in 0.6 to 1.0 m layer; and slowly increases in 1.0 to 1.6 m layer. The average soil water content reached the lowest at the mid-slope point. The SWC in every soil layer showed strong temporal stability. With the increase of soil layer, the degree of temporal stability became stronger. The representative position of SWC varies with increasing depth. There was no significant difference root mean square error (RMSE) and mean absolute error (MAE) values of the eight layers for both the period of entire year and monsoonal (p > 0.05), which suggested that seasonal variation had no significant effect on the SWC of different locations. The best representative location could accurately assess the average SWC. The mean relative difference (MRD) was significantly positively correlated with the content of clay and silt and significantly negatively correlated with the content of sand. The clay content, silt content, elevation, soil organic and root density significantly positively affected the standard deviation of the relative differences (SDRD). In conclusion, SWC exhibited strong spatial and temporal patterns at different soil depths and hillslope locations after rainfall.

Keywords: Soil water content, Forestland, Temporal stability, Spatial distribution, Hillslope.

摘　　　　　要

土壤含水量在生態水文過程中起著重要作用，是黃土高原植被重建的首要限制性因素。因此，掌握坡面土壤含水量的時空動態變化是十分必要的。本文調查研究了雨季時期黃土高原坡面林木的土壤水分的空間分佈和時間穩定性，進一步分析了影響其時空穩定性的因素。在生長著中國油松的坡面上選取了21個監測點，沿0-1.6 m的土壤剖面來測量8個土層的含水量。結果表明：所有土層的土壤含水量較低，且呈現中等變異。土壤含水量在0-1.6 m的土壤深度呈現3種變化趨勢：0-0.6 m土層呈現增長趨勢，0.6-1.0 m土層呈現下降趨勢，1.0-1.6 m土層呈現緩慢增長趨勢。坡面中間位置監測點的平均土壤含水量最低。土壤含水量在空間上呈現出強烈的時間穩定性，且隨著土壤深度的增加土壤含水量的時間穩定性增強。土壤含水量的代表性位置點隨著土壤深度的增加而變化。全年和雨季期在8個土層的土壤含水量沒有顯著性差異。最佳代表性位置點能夠準確地評價區域平均土壤含水量。平均相對差分與土壤黏粒和砂粒含量分別呈現顯著的正和負的相關性。土壤粉粒，黏粒，高程，土壤有機碳和根系密度顯著影響相對差分的標準差。總之，雨後土壤含水量在不同土壤深度和坡面位置上呈現出強烈的時空穩定性。

關鍵詞： 土壤含水量，林地，時間穩定性，空間分佈，山坡。

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put forward the notion of the temporal stability of SWC spatial patterns, which implied the extent of SWC spatial patterns variation with time; a stronger temporal stability showed that the SWC pattern changed little with time though the practical soil water content value was possible to change greatly in a given area.

The temporal stability concept has been successfully applied to representative positions that delegate the average SWC of a given area, to up or down scale SWC estimate and to offer lost data in process of hydrological analyses (Schneider et al., 2008; Liu et al., 2014), which can economize time and labor compared to sampling randomly at a lot of sites (Jia et al., 2013). Many researchers who studied the temporal stability of soil water spatial pattern have been implemented with different focuses on the Loess Plateau of China. In terms of scale, the characteristic of temporal stability of SWC has been investigated at the watershed (Brocca et al., 2010), hillslope (Gao et al., 2012; Jia et al., 2013a) and plot scales (Jia et al., 2013b). Some researchers also studied the effects of soil vertical depth on temporal stability on the Loess Plateau (Brocca et al., 2010; Gao et al., 2012; Jia et al., 2013a; Martinez). However, many external factors affect the temporal stability of SWC, e.g. topography, rainfall, vegetation, soil properties and organic carbon (Joshi et al., 2011; Wang et al., 2015; Zhu et al., 2011). To exactly identify the representative positions of average SWC, several studies have investigated the main factors effecting the temporal stability of SWC (Hu et al., 2010). For example, da Silva et al. (2001) observed the effect of organic carbon and clay content on soil water stability was greater than that of topography. Mohanty and Skaggs (2001) reported that silty loam showed reduced temporal stability comparing with sandy loam. Hu et al. (2009) found that soil particle size and organic matter content were the main factors influencing temporal stability. The temporal stability of SWC is also related to dry and wet soil

conditions (Gao et al., 2012; Jia et al., 2013b). For instance, Martínez-Fernández and Ceballos (2003) observed that the patterns of SWC were more stable under dry conditions in a semiarid area. However, Zhao et al. (2010) found that the patterns of SWC were more stable over time during wet seasons, and less stable during dry seasons. In addition, Jia and Shao et al. (2013a) found that the temporal stability of SWC was obviously influenced by vegetation type and variation, particularly in upper soil.

On the Loess Plateau of China, SWC is the primary restrictive factor for the vegetation restoration. Understanding the spatial distribution and temporal stability of SWC is necessary to fully understand the vegetative carrying capacity and vegetative restoration processes on the Loess Plateau. As result of strong soil erosion and human activities, the ecological environment in the northern Loess Plateau is deteriorating daily (Tang et al., 1993). Since the late 1990s, a vegetative restoration project led by the Government was implemented to improve the ecological environment and to control the water and soil loss; the project has resulted in the conversion of cropland to scrubland, grassland, or forest (Chen et al., 2008; Liu et al., 2015). There were more than 16,000 km2 of rain-fed cropland on the Loess Plateau into forest land or grassland. However, these measures have highlighted the contradiction between the plant water demand and available soil water. Several kinds grew well in the early stages, but they began to decline during the later stages result from lacking of water (Liu et al., 2015). Some investigators have suggested that vegetation restoration results in drier soil (Yaseef et al., 2013; Wang et al., 2010; Cao et al., 2011). Moreover, soil water consumption and supply also primarily occur during the monsoon. However, fewer the above mentioned research got whole profile distribution features of temporal stability indices and whether the temporal stability of the SWC is influenced by soil depth and plant root activity for sloped forests during the monsoon.

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In this research, the spatial patterns and temporal stability of SWC are analyzed at eight soil layers at 21 locations up on a hillslope of the growth of the Chinese pine (Pinus tabulaeformis Carr.), that is one of the major kinds applied for vegetation restoration on the Loess Plateau. We then examine whether the SWC from the best representative locations could accurately evaluate average SWC at different soil depths and tested the main factors affecting temporal stability of SWC on the hillslope forests. The aims of this study are to: (1) determine representative locations in different soil layers; and (2) evaluate the factors effecting the temporal stability of SWC during the monsoon.

2. MATERIALS AND METHODS

2.1 The study area

The research location site is a semiarid loessal hillslope in the Wangmaogou catchment (110°20′26"–110°22′46"E, 37°34′13"–37°36′03"N), Suide County, Loess Plateau, China (Fig. 1). The hillslope is approximately 60 m long and 30 m wide, with an average gradient of 33°. The altitude ranged

from 967 m to 997 m.a.s.l. The forest covering the hillslope is approximately 29 years old, with Chinese pine planted every 1.85 m. The soil in the study area is a silt loam (USDA) with 0.02% clay, 65.28% silt and 34.70% sand. The soil bulk density of upper is 1.1–1.3 g/cm3. The regional climate is classified as monsoonal, with an average annual temperature and precipitation of 10.2°C and 513 mm, respectively; more than 60% of the rainfall falls during July to September.

2.2 Measurement

The SWC was measured by TRIME-TDR access tubes (0.044 m diameter, Polycarbonate) that were installed at 21 positions up the hillslope (Fig. 1c). Regarding their possible edge effects and slope site, the study did not investigate the variation in SWC of positions 1, 2, and 9 along the hillslope. The tubes were spaced 10 m apart. The properties of root and soil were evaluated as putative predictors of variation in temporal stability of SWC. At each of the18 locations, soil samples with roots were collected at 0.2-m intervals down to a depth of 1.6 m. The 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, 0.8-1.0, 1.0-1.2, 1.2-1.4, and 1.4 - 1.6 m soil layers were defined as D1, D2, D3, D4, D5, D6, D7 and D8. At each of the18 locations, eight layers of soil samples were collected with roots. The root length (cm) and density (cm/cm3) were analyzed by a WinRHIZO 2013 image analyzer system (Regent Instruments Inc., Quebec, Canada). The soil particle composition was measured using a Mastersizer 2000 particle size analyzer (Malvern Instruments, Malvern, UK). A multi N/C 3100 analyzer (Analytik Jena AG, Jena, Germany) was used to determine SOC content. TN and TP of soil samples that had been passed through a 1.0-mm sieve were analyzed with an Auto Discrete Analyzer (ADA, Clever Chem 200, Hamburg, Germany).

At least 13 different dates should be collected to evaluate potential change in the spatial pattern for the samples of soil water (Wang et al., 2010;

Fig. 1. The location and layout of the study site. (a) Loess Plateau, China. (b) Wangmaogou watershed. (c) Location and layout of the 21 sample points in the study site.

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Martínez-Fernández et al., 2003). Volumetric SWC (% v/v) was measured in eight soil layers at each of the 18 locations from July, 2014 to September, 2015. For each month, the SWC was measured twice, with the exceptions of October and November of 2014 and February of 2015 (measured only once a month), which was defined as the entire year period. The SWC was measured once a day in the monsoon period (19/7/2014-03/9/2014 and 01/8/2015-31/8/2015). The Fig. 2 illustrated distribution of precipitation during the study period.

2.3 Data analysis

2.3.1 Evaluating the temporal stability of SWC

The temporal stability of SWC is examined by the Spearman’s rank correlation test and a relative difference analysis.

The nonparametric Spearman rank order correlation (rs) was calculated using Equation 1:

(1)

where Rij is the rank of the SWC at location i and time j, and Rik is the rank of the SWC at the same location but for another time, k (k≠j). The ranks of the SWCs are deemed to be similar when The Closing to 1 of the rs values indicated a similar rank of the SWCs and strong temporal persistence of

spatial patterns.The second method is based on the relative

differences analysis (Vachaud et al. 1985), SWCij is assumed to be at location i (i = 1, ... , n) and time j (j = 1, ... , m), and the relative difference δij is calculated using Equation 2:

(2)

where SWCj is the spatial average value for the time j with n locations (Equation 3):

(3)

The temporal average relative difference (MRD) and its standard deviation (SDRD) are calculated using Equations 4 and 5, respectively:

(4)

and

(5)

where m is the number of sampling dates.The root average square error (RMSE) is a

frequently used measure in difference between values estimated and the values observed. Predictive accuracy is evaluated using the mean absolute error (MAE). The MAE and the RMSE are obtained depend on Equations 6 and 7:

(6)

(7)

where Pi and pi are the estimated and measured values, respectively.

2.3.2 Statistical analysis

The coefficient of variation (CV) of the SWC results is applied to analyze the temporal and

Fig. 2. Distribution of precipitation in the study area from July to September in 2014 and 2015.

rs = 16 (Rij Rij )2n

i =1

n (n2 1)

ij =SWCij SWCj

SWCj

=1n

n

i =1SWCj SWCij

=1m

m

j =1i ij

( ) =1

m 1( )2

m

j = 1i iij

MAE =1n

Pi pi ×100%n

i = 1

= ( )2n

i = 1

1n Pi pi × 100%RMSE

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spatial variability of the SWC. One-way analysis of variance (ANOVA) is used to analyze the correlation between the MRD, SDRD and influences factors of temporal stability of SWC. The maximum, minimum, and average SWC are analyzed using SPSS 16.0. The linear regression analysis is carried out using the OriginPro 8.5. The figure of location and layout of the study site are drawn by the GIS software ArcMap (version 9.3).

2.4 The method for identifying representative locations

Identifying representative locations in this study is mainly based on the procedure originally proposed by Vachaud et al. (1985). The temporally stable locations should be those with the MRD closest to zero and with the minimum associated standard deviations. Most studies (Grayson et al., 1998; Hu et al., 2010; Liu et al., 2014; Schneider et al., 2014) have preferred this method because the locations identified is capable to directly stand for the average SWC value of a given area. The positions with MRD values within ±5% are considered as close to zero (Jacobs et al., 2014;

Schneider et al., 2014). In addition, the temporally stable locations must have low values of SDRD. To quantify this condition, we chose the locations with associated standard deviations lower than the average values. The standard two-tailed t-test is performed to test the hypothesis that the average values of the chosen locations are obviously different from zero; those that are significantly different at the 95% confidence level are rejected as temporally stable locations.

3. RESULTS

3.1 The distribution characteristics of SWC

3.1.1 The vertical distribution characteristics of SWC

From Table 1, statistics of the SWC over the research stage at each soil depth could be found. The average SWCs were low at all soil depths. The SWCs of the eight soil depths for 07/19/14–09/03/14 and 08/01/15–08/31/15 changed from 0% to 22.7% and 0.33% to 26.4%, respectively, which were lower than the saturated SWC (45%) and the field capacity

Table 1. Summary statistics of soil water content throughout the study period at each soil deptha

Period Depth (m) Average (%) Min. (%) Max. (%) CV (%)07/19/14–09/03/14 L1 (0.0–0.2) 6.62 0 17.64 54

L2 (0.2–0.4) 10.76 0 22.47 41L3 (0.4–0.6) 13.26 2.73 22.74 25L4 (0.6–0.8) 11.38 6.85 20.68 25L5 (0.8–1.0) 10.57 7.12 18.44 23L6 (1.0–1.2) 11.03 7.41 18.71 20L7 (1.2–1.4) 11.59 7.16 19.5 22L8 (1.4–1.6) 12.07 6.75 19.26 21

08/01/15–08/31/15 L1 (0.0–0.2) 7.35 0.33 19.90 41 L2 (0.2–0.4) 9.81 0.46 26.40 37 L3 (0.4–0.6) 10.78 2.16 20.50 19 L4 (0.6–0.8) 9.82 7.36 16.68 14 L5 (0.8–1.0) 9.48 7.37 13.27 12 L6 (1.0–1.2) 9.73 7.29 13.54 13 L7 (1.2–1.4) 10.05 6.71 13.97 14 L8 (1.4–1.6) 10.20 6.32 15.90 15

aMin., minimum value; Max., maximum value; CV, coefficient of variation.

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(25%). SWC showed three trends: increasing from D1 to D3; decreasing from D4 to D5; and slowly increasing from D6 to D8. The minimum SWCs of the top three soil layers were obviously lower than those of other five deeper soil layers (p < 0.01). The lowest maximum SWC and lowest average SWC were both in D1. The highest average SWC occurred at D3 from 07/19/14 to 09/03/14, suggesting that more soil water was conserved in this layer during this period. However, the highest average SWC occurred in D2 from 08/01/15 to 08/31/15, suggesting that more soil water was conserved at this depth during this period. The standard errors of SWC at each layer were small and showed little difference. The coefficient of variation (CV) is a standardized measure of dispersion of a probability distribution or frequency distribution, which was used to qualitatively evaluate the degree of spatial variability. The SWC of the eight soil layers showed moderate spatial variability, with CV values changing from 20% to 54% for 07/19/14–09/03/14 and from 12% to 41% for 08/01/15–08/31/15. The spatial variability in upper soil layers (D1 and D2) was obviously larger than in the deeper soil layers (L3–L8) for the two times periods (p < 0.01).

3.1.2 Characteristics of SWC distribution at different slope positions

The average SWC of different slope positions is

shown in Table 2. Locations 3–8, 10–15, and 16–21 denoted the upper slope, mid-slope, and lower slope, respectively. The average SWC values of different slope positions showed the same trend in the different soil depths. The average SWC was lowest in the mid-slope, whereas the total root length was the highest. Average SWC was negatively correlated with total root length (p < 0.01). Moreover, average SWC at different slope positions increased as the soil depth increased in 0–0.6 m, 0-1.0 m and 0–1.6 m. The total root length at different slope positions decreased as the soil depth increased in 0–0.6 m, 0-1.0 m and 0–1.6 m. However, the distribution of soil particle size and SOC at different slope positions did not show an obvious correlation with the average SWC. The clay content and sand content increased along the hillslope, whereas the silt content decreased. The average SOC at different slope positions generally decreased as the soil depth increased in 0–0.6 m, 0-1.0 m and 0–1.6 m.

3.2 Temporal stability characteristics of the SWC profile

3.2.1 Nonparametric Spearman’s rank correlations

The time series of average rs in diverse depths during 07/19/14 to 08/31/15 and 07/19/14 to 09/03/14 were shown in Fig. 3. Moreover, the values of rs of the eight soil layers from 07/19/14

Table 2. Average soil water content at different slope positions from July 19 to September 3, 2014

Depth (m)Sample location

Average value

SWC (%)Total root

length (cm)Clay (%) Silt (%) Sand (%) SOC (g/kg)

0–0.6 3–8 9.90 237.51 0.20 68.43 31.37 14.5410–15 9.20 343.91 0.22 67.09 32.69 13.7016–21 9.91 277.30 0.25 62.56 37.20 9.67

0–1.0 3–8 10.24 183.44 0.19 68.48 31.33 13.8310–15 9.21 245.19 0.21 67.30 32.50 13.3016–21 10.49 206.75 0.24 61.49 38.28 10.06

0–1.6 3–8 10.57 127.37 0.18 68.14 31.68 13.5210–15 10.12 171.39 0.21 66.93 32.86 13.0616–21 11.23 139.96 0.22 60.59 39.19 9.43

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to 08/31/15 ranged from 0.41 to 0.96. The value of average rs of each soil layer for the monsoon (07/19/14 to 09/03/14) was greater than that of the corresponding layer for the entire year period (07/19/14 to 08/31/15), demonstrating that the SWC spatial pattern of the monsoon indicated stronger temporal stability. There were significant differences in rs at different soil depths (p < 0.05). The changes in rs with soil layer indicated the SWC temporal spatial distribution became stronger as the soil depth increased (Fig. 4). However, in the upper 0.8-m layer, rs initially fluctuated. The temporal stability consistently tended to increase from 0.8m down to 1.2 m. Below 1.2 m, the temporal stability of SWC became more stronger and relatively constant; all the rs values were higher than 0.94. The temporal stability of SWC at deeper soil depths was stronger

than that at the 0–0.4 m soil depths.

3.2.2 Representative sampling locations and estimation accuracy

The SWC of MRDs and SDRDs depended on the relative difference analysis at different soil layers were presented in Figure 5. The ranges of MRD for 07/19/14–08/02/14 from D1 to D8 were: −0.64 to 0.36, −0.59 to 0.57, −0.34 to 0.37, −0.27 to 0.55, −0.25 to 0.56, −0.31 to 0.60, −0.32 to 0.60, and −0.33 to 0.51, respectively. The ranges of MRD in D1 and D2 were significantly larger than that in D3–D9 (p < 0.01). The range of MRD was smallest in D3. The average SDRDs for 07/19/14–08/31/15 from D1 to D8 were 0.10, 0.10, 0.05, 0.04, 0.07, 0.02, 0.02, and 0.02, respectively. The low, stable average SDRD below a soil depth of 1.0 m indicated that SWC of deeper soil layers was more stable than that in upper soil layers. Similar results have been observed in orchard, cropland (Gao et al., 2012; Martínez-Fernández et al., 2003). All the locations showed consistently higher or lower SWC than the average SWC at different soil layers. The number

Fig. 3. Average Spearman rank correlation coefficients for each sampling date compared with the other sampling dates at each soil depth.

Fig. 4. Relation between soil depth and the average Spearman correlation coefficient.

Fig. 5. Average relative differences in soil water content at each soil depth (L1–L8): representative locations calculated for 07/19/14–08/02/14. Vertical bars represent the standard deviation. The best representative locations of each soil depth are marked in black.

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of representative locations was more than one for each soil layer. Differences occurred within the best representative locations at different soil depths. For example, the best representative locations of D1, D5, and D7 were all at location 18, whereas the best representative location of D8 was at location 4. The best representative locations of D2, D3, D4, and D6 were at location 12, location 15, location 6, and location 14, respectively.

Fig. 6 showed the estimation accuracy of the representative locations for the eight soil depths. The range of coefficient of determination (R2) were different for the entire year period (0.50 to 0.95, Fig. 6a) and the monsoon (0.72 to 0.95, Fig. 6b) but all the fitting equations were showed significantly (p < 0.01). Compared with the estimated values and the measured values, the smaller values of RMSE and MAE are, the smaller differences and higher predictive accuracy represent. For the estimation analysis from 07/19/14 to 08/31/15 (Fig. 6a), the values of MAE changed from 0.26% to 0.83%, with an average value of 0.53%; the values of RMSE changed from 0.31% to 1.2%, with an average value of 0.68%. The SWCs over the study period were used to examine the predictive accuracies of the best representative locations identified based on the SWC from 07/19/14 to 08/04/14 and 08/01/15 to 08/31/15 (Fig. 5b). The values of MAE changed from 0.26% to 0.83%, with an average value of

0.53%; the values of RMSE changed from 0.31% to 1.06%, with an average value of 0.67%. The average values of MAE and RMSE were similar between entire year period and monsoon. ANOVA indicated that the MAE values and RMSE values during the two periods were not obviously different (p > 0.05).

3.3 Factors affecting the temporal stability of SWC

The correlation between the MRD, SDRD and the effecting factors were analyzed (Table 3). Elevation, TN, TP and SOC did not exhibit a significant correlation with MRD in each soil layer. MRD was positively correlated with silt content and negatively correlated with sand content in D2 (p < 0.05). MRD was significantly positively correlated with clay content (p < 0.05) in D7. The Pearson correlation coefficient between MRD and root density was only significant at D8. MRD showed a negative correlation with root density in D8, which suggested that locations with greater root density had lower temporal stability (p < 0.05). MRD showed a negative correlation with the content of sand and positive correlation with the content of clay and silt (p < 0.01) for 0~1.6 m soil depth, suggesting that locations with moderate silt and clay contents were more temporally stable.

Elevation exhibited a significant positive correlation with SDRD from L3 to L6, suggesting that the distribution of SWC was obviously impacted by elevation along the forested hillslope. A similar relation was also observed between SDRD and elevation in DI, D2, D7, and D8, but the correlations were not significant (p > 0.05). SOC showed a significant positive relation with SDRD in D3 (p < 0.05). SDRD was positively correlated with SOC from DI to D2 and negatively correlated from L4 to L8, but the correlations were not significant (p > 0.05). SDRD had not obviously positively correlated with TN, TP and root density at most soil layer (p > 0.05). SDRD had negative correlation with the content of sand and positive correlation with the content of clay and silt (p <

Fig. 6. Comparison between the average soil water content and the soil water content of the representative locations at different soil depths over the entire monitoring period: representative locations calculated for entire year period (a) and monsoon season (b).

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0.01), suggesting that locations with moderate silt and clay contents were more temporally stable. The clay content, silt content, elevation, soil organic and root density were significantly positively affected the standard deviation of the relative differences for 0~1.6 m soil depth.

4. DISCUSSION

4.1 The temporal variability of SWC

The study hillslope located in a semi-arid region that has relative low precipitation and strong evapotranspiration, which lead to low SWC on forested slopes soil. On the Loess Plateau, some researchers also reported similar results in different vegetation types (Gao et al., 2012; Wang et al., 2015). The steepness of a site can reduce the water retention capacity of the soil because of downslope drainage (Gao et al., 2012). From the Table 1, the SWC of D5 was the lowest except D1, which

showed that the rate of root water uptake was greater than water in infiltration at this soil layer during monsoon season. Furthermore, Wang et al. (2010) reported that the shallow soil water (0–20 cm depth) was mainly absorbed by the roots during the wet season. The average SWCs of the upper, middle, and lower slopes were 10.57%, 10.12%, and 11.23% at a soil depth of 0–1.6 m in the monsoon. The minimum average SWC in the mid-slope was primarily the result of competition between roots, in that the total root length was highest and consumed more soil water in this site compared with the upper or lower slope sites. The average total root lengths of the upper, middle, and lower slopes were 127.37, 171.39 and 139.96 cm at a soil depth of 0–1.6 m, respectively. The highest SWC occurred at lower slope due to down slope as an area of collection flow.

4.2 The representative location of SWC distribution patterns

Table 3. Pearson correlation coefficients between MRD, SDRD, and variables for the eight soil depthsa

DepthElevation

(m)SOC (g/kg)

TN (g/kg)

TP (g/kg)

Clay (%)

Silt (%)

Sand (%)

Root density (cm/cm³)

MRD

L1 .302 .014 .234 .090 .087 .397 -.395 -.284L2 .151 .291 .225 .020 .211 .462* -.463* -.282L3 .056 .138 -.342 .285 .129 .200 -.201 -.093L4 .127 -.136 -.311 -.212 .378 .147 -.150 -.115L5 .082 -.054 .115 .068 .224 .272 -.274 -.337L6 -.045 -.333 -.095 -.058 .156 .415 -.415 -.331L7 -.029 -.368 .061 -.053 .549* .352 -.356 -.337L8 .039 -.267 .021 .405 .347 .360 -.362 -.479*

0~1.6 m .103 -.032 .025 .037 .221** .274** -.276** -.127

SDRD

L1 .298 .017 .077 -.011 -.057 .374 -.368 -.334L2 .348 .357 .237 .256 .057 .322 -.321 -.130L3 .563** .469* .064 .168 -.352 .049 -.044 -.135L4 .504* .118 -.158 -.134 .404 .357 -.359 -.017L5 .554** .133 -.093 .225 -.057 .255 -.254 -.379L6 .475* .267 .016 .141 -.046 .316 -.316 -.068L7 .382 -.140 -.093 .042 .401 .468* -.470* -.263L8 .352 -.137 .100 .165 .026 .231 -.230 -.207

0 ~ 1.6 m .263** .318** .072 .056 .205** .230** -.231** .211**a*. Correlation is significant at the 0.05 level (2-tailed); **. Correlation is significant at the 0.01 level (2-tailed); CV, coefficient of variation of soil water content.

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The ranges of MRD in D1 and D2 were obviously larger than that in other soil depths (p < 0.01), which corresponded with a higher CV value (Table 1). The range of MRD recorded in the current study was close to that found by Gao et al. (2011), but lower than that reported by Hu et al. (2010). This might be attributed to the former two studies considered hillslope sites, whereas Hu et al. (2010) used a watershed site, resulting in more spatial variability effects on SWC. Other researchers reported that the representative locations varied from upper to deeper soil layer (Coppola et al., 2011; Martínez-Fernández et al., 2003; Guber et al., 2008; Xu et al., 2015). The average value of SDRD decreased with increasing soil depth, which suggested that the standard deviation of SWC decreased with increasing soil depth. These results were similar to those of other studies (Liu et al., 2014; Guber et al., 2008; Penna et al., 2011).

The rs values of the deeper soil layers was lower than that of the top soil layers. The stronger impact of climatic, biological, and hydrological factors at the 0–0.4-m soil depth resulted in the relatively weaker temporal stability of SWC compared to in deeper soil depths. These results were similar to those of Kamgar et al. (1993) and Hu et al . (2010), who reported that the temporal stability of SWC in deeper soil layer was significantly higher than that in the top soil layer. Moreover, the larger range of MRD for the 0–0.4 m soil depth corresponded with the lower average SWC and the larger spatial variability in D1 and D2. The smallest range of MRD occurred in D3, which corresponded with the largest average SWC and relatively low spatial variability (Table 1 and Figure 3). However, former researchers have almost none surveyed the features of temporal stability of SWC through a whole hillslope forested profile.

The RMSE and MAE values were not obviously different between the entire year period and monsoonal for all the soil layers (p > 0.05), which suggested that seasonal variation had no

significant effect on the SWC of the different locations, the average SWC of the hillslope, or the spatial pattern of SWC at different soil depths. Thus, it was possible to accurately predict the average SWC based on the SWC of representative locations, a result that was similar to that of Liu and Shao (2014). The coefficients of determination (R2) during monsoon season was higher than over the entire year period since the spatial patterns of SWC in monsoon season showed stronger temporal stability than over the entire year period. However, the SWC was accurately estimated by using the representative locations since the MAE and the RMSE were <1% and <2% (Cosh et al., 2008), respectively. Therefore, the estimations were credible because the largest RMSE was 1.02% for the eight soil depths. The predictive accuracies were acceptable compared to other research results (Hu et al., 2010; Brocca et al., 2010; Gao et al., 2012).

4.3 The major influencing factors of temporal stability of SWC

Many researchers have surveyed the correlation between SWC, the temporal stability of SWC, and the affecting factors (Chaney et al., 2015; Gao et al., 2012; Vachaud et al., 1985; Xu et al., 2015). However, the factors influencing SWC, MRD, and SDRD were probable different. The relationship between factors and MRD, SDRD have seldom been researched separately. In this study, the distribution of soil particles had a significantly influenced relative to elevation, SOC, TN and TP for monsoon period, since soil particles was a vital factor affecting soil water conservation and evaporation (Xie et al., 2010).

In the current s tudy, the elevation has nonsignificant effect on the SWC. Similar results were also reported by Zhao et al. (2011) and Hébrard et al. (2006). However, a significantly negative relationship between SWC and elevation has been reported by other studies (Gao et al., 2012; Zhao et al., 2010). The difference between these

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results was primarily due to the different elevations and vegetation types across the study sites. In contrast to the results of other studies (Biswas et al., 2001; Manns et al., 2014; Zhao et al., 2010), who reported strong positive correlations between SOC and MRD, no significant correlation was found between these two variables in the current study. However, this result was concordant with those of other studies (Gao et al., 2012; Schneider et al., 2014). In the present study, a negative relation was observed between MRD and root density at the eight soil depths but showed no significant correlation (p > 0.05). The negative relation between MRD and different root densities was likely to be the result of water uptake by the roots. The relationship between soil particle size and MRD was consistent with that reported by other studies (Biswas et al., 2001; Gao et al., 2012; Manns et al., 2014).

The representative location must be a lower SDRD, which indicated the stronger temporal stability of SWC. Some researchers have studied the relationship with SDRD and related variables. In this study, a significant relation was found between SDRD and elevation, SOC, root density, and soil particle size in 0-1.6 m soil depth (p < 0.01). These results might be attributed to the effect of elevation on the timing and amount of solar radiation, which would result in different levels of evaporation. Water uptake by roots also had a significant effect on SWC (Wang et al., 2012).

It is possible to apply the representative locations of SWC to predict the average SWC during a time period. One problem is that other variables still need to be further investigated and studied, such as soil texture, root size, and so on. Additionally, it is difficult to apply the predictive equations to different vegetation types or regions because related variables are not consistent across study areas (Schneider et al., 2012). The related variables contributing to soil water variability also change over different time and spatial scales. The patterns of the temporal stability of SWC have also

been reported to change to some extent (Gao et al., 2012; Martínez-Fernández et al., 2003; Zhao et al., 2010).

5. CONCLUSIONS

The average SWCs were low and demonstrated moderate spatial variability for all the soil layers. SWC in three soil depths were observed: SWC increases when the depth was 0 to 0.6 m; decreases in 0.6 to 1.0 m layer; and slowly increases in 1.0 to 1.6 m layer. The average SWC in different slope positions showed lower slope > upper slope > mid-slope. The temporal stability SWC was strong at each soil depth. The temporal stability of SWC at deeper soil depths was stronger than that at the 0–0.4 m soil depths. There was no obvious difference between RMSE and MAE values of the eight layers for the entire year period and monsoonal period (p > 0.05), which suggested that seasonal variation had no significant effect on the SWC of different locations. The representative location of SWC was not constant in different soil depths. It is possible to apply the representative locations of SWC to predict the average SWC of the hillslope over a set time period. MRD showed a negative correlation with the content of sand and positive correlation with the content of clay and silt (p < 0.01) for 0~1.6 m soil depth. The clay content, silt content, elevation, soil organic and root density significantly positively affected the SDRD in 0~1.6 m soil depth. Consequently, the SWC in different soil depths and locations on the hillslope with Chinese pine indicated strong temporal stability following rainfall. However, a priori identification of locations of temporal SWC stability based on relevant variables is difficult.

ACKNOWLEDGEMENTS

This project was supported by the National Key Research and Development Program of China

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(No. 2016YFC0402404); the National Natural Science Foundation of China (Nos. 51779204 and 41601291); Shaanxi Provincial Technology Innovation Guidance Project (2017CGZH-HJ-06); the School foundation of Xi’an University of Technology (No.310-252071711). The authors wish to acknowledge the members of the project team for investigation and sampling in the field.

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Received: 108/01/22

Revised: 108/03/06

Accepted: 108/04/15